"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F

__all__ = ['MuDeep']


class ConvBlock(nn.Module):
    """Basic convolutional block.
    
    convolution + batch normalization + relu.

    Args:
        in_c (int): number of input channels.
        out_c (int): number of output channels.
        k (int or tuple): kernel size.
        s (int or tuple): stride.
        p (int or tuple): padding.
    """

    def __init__(self, in_c, out_c, k, s, p):
        super(ConvBlock, self).__init__()
        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
        self.bn = nn.BatchNorm2d(out_c)

    def forward(self, x):
        return F.relu(self.bn(self.conv(x)))


class ConvLayers(nn.Module):
    """Preprocessing layers."""

    def __init__(self):
        super(ConvLayers, self).__init__()
        self.conv1 = ConvBlock(3, 48, k=3, s=1, p=1)
        self.conv2 = ConvBlock(48, 96, k=3, s=1, p=1)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.maxpool(x)
        return x


class MultiScaleA(nn.Module):
    """Multi-scale stream layer A (Sec.3.1)"""

    def __init__(self):
        super(MultiScaleA, self).__init__()
        self.stream1 = nn.Sequential(
            ConvBlock(96, 96, k=1, s=1, p=0),
            ConvBlock(96, 24, k=3, s=1, p=1),
        )
        self.stream2 = nn.Sequential(
            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
            ConvBlock(96, 24, k=1, s=1, p=0),
        )
        self.stream3 = ConvBlock(96, 24, k=1, s=1, p=0)
        self.stream4 = nn.Sequential(
            ConvBlock(96, 16, k=1, s=1, p=0),
            ConvBlock(16, 24, k=3, s=1, p=1),
            ConvBlock(24, 24, k=3, s=1, p=1),
        )

    def forward(self, x):
        s1 = self.stream1(x)
        s2 = self.stream2(x)
        s3 = self.stream3(x)
        s4 = self.stream4(x)
        y = torch.cat([s1, s2, s3, s4], dim=1)
        return y


class Reduction(nn.Module):
    """Reduction layer (Sec.3.1)"""

    def __init__(self):
        super(Reduction, self).__init__()
        self.stream1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.stream2 = ConvBlock(96, 96, k=3, s=2, p=1)
        self.stream3 = nn.Sequential(
            ConvBlock(96, 48, k=1, s=1, p=0),
            ConvBlock(48, 56, k=3, s=1, p=1),
            ConvBlock(56, 64, k=3, s=2, p=1),
        )

    def forward(self, x):
        s1 = self.stream1(x)
        s2 = self.stream2(x)
        s3 = self.stream3(x)
        y = torch.cat([s1, s2, s3], dim=1)
        return y


class MultiScaleB(nn.Module):
    """Multi-scale stream layer B (Sec.3.1)"""

    def __init__(self):
        super(MultiScaleB, self).__init__()
        self.stream1 = nn.Sequential(
            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
            ConvBlock(256, 256, k=1, s=1, p=0),
        )
        self.stream2 = nn.Sequential(
            ConvBlock(256, 64, k=1, s=1, p=0),
            ConvBlock(64, 128, k=(1, 3), s=1, p=(0, 1)),
            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
        )
        self.stream3 = ConvBlock(256, 256, k=1, s=1, p=0)
        self.stream4 = nn.Sequential(
            ConvBlock(256, 64, k=1, s=1, p=0),
            ConvBlock(64, 64, k=(1, 3), s=1, p=(0, 1)),
            ConvBlock(64, 128, k=(3, 1), s=1, p=(1, 0)),
            ConvBlock(128, 128, k=(1, 3), s=1, p=(0, 1)),
            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
        )

    def forward(self, x):
        s1 = self.stream1(x)
        s2 = self.stream2(x)
        s3 = self.stream3(x)
        s4 = self.stream4(x)
        return s1, s2, s3, s4


class Fusion(nn.Module):
    """Saliency-based learning fusion layer (Sec.3.2)"""

    def __init__(self):
        super(Fusion, self).__init__()
        self.a1 = nn.Parameter(torch.rand(1, 256, 1, 1))
        self.a2 = nn.Parameter(torch.rand(1, 256, 1, 1))
        self.a3 = nn.Parameter(torch.rand(1, 256, 1, 1))
        self.a4 = nn.Parameter(torch.rand(1, 256, 1, 1))

        # We add an average pooling layer to reduce the spatial dimension
        # of feature maps, which differs from the original paper.
        self.avgpool = nn.AvgPool2d(kernel_size=4, stride=4, padding=0)

    def forward(self, x1, x2, x3, x4):
        s1 = self.a1.expand_as(x1) * x1
        s2 = self.a2.expand_as(x2) * x2
        s3 = self.a3.expand_as(x3) * x3
        s4 = self.a4.expand_as(x4) * x4
        y = self.avgpool(s1 + s2 + s3 + s4)
        return y


class MuDeep(nn.Module):
    """Multiscale deep neural network.

    Reference:
        Qian et al. Multi-scale Deep Learning Architectures
        for Person Re-identification. ICCV 2017.

    Public keys:
        - ``mudeep``: Multiscale deep neural network.
    """

    def __init__(self, num_classes, loss='softmax', **kwargs):
        super(MuDeep, self).__init__()
        self.loss = loss

        self.block1 = ConvLayers()
        self.block2 = MultiScaleA()
        self.block3 = Reduction()
        self.block4 = MultiScaleB()
        self.block5 = Fusion()

        # Due to this fully connected layer, input image has to be fixed
        # in shape, i.e. (3, 256, 128), such that the last convolutional feature
        # maps are of shape (256, 16, 8). If input shape is changed,
        # the input dimension of this layer has to be changed accordingly.
        self.fc = nn.Sequential(
            nn.Linear(256 * 16 * 8, 4096),
            nn.BatchNorm1d(4096),
            nn.ReLU(),
        )
        self.classifier = nn.Linear(4096, num_classes)
        self.feat_dim = 4096

    def featuremaps(self, x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(*x)
        return x

    def forward(self, x):
        x = self.featuremaps(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        y = self.classifier(x)

        if not self.training:
            return x

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, x
        else:
            raise KeyError('Unsupported loss: {}'.format(self.loss))
